# Hierarchical Needs-driven Agent Learning Systems: From Deep Reinforcement Learning To Diverse Strategies

arXiv: https://arxiv.org/abs/2302.13132 · HTML: https://mlsanity.com/paper/2302.13132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [dm_control: Software and Tasks for Continuous Control](https://mlsanity.com/paper/2006.12983/) (2020)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [From Motor Control to Team Play in Simulated Humanoid Football](https://mlsanity.com/paper/2105.12196/) (2021)
- [Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review](https://mlsanity.com/paper/2102.04148/) (2021)
- [Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System](https://mlsanity.com/paper/2004.10920/) (2020)
- [Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions](https://mlsanity.com/paper/2009.00288/) (2020)
- [How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model](https://mlsanity.com/paper/2105.07443/) (2021)
- [Game-theoretic Utility Tree for Multi-Robot Cooperative Pursuit Strategy](https://mlsanity.com/paper/2206.01109/) (2022)
- [A Game-Theoretic Utility Network for Cooperative Multi-Agent Decisions in Adversarial Environments](https://mlsanity.com/paper/2004.10950/) (2020)

## Built on by (most cited first, 1 of 1)

- [Edge Computing based Human-Robot Cognitive Fusion: A Medical Case Study in the Autism Spectrum Disorder Therapy](https://mlsanity.com/paper/2401.00776/) (2024)

## Abstract (arXiv)

The needs describe the necessities for a system to survive and evolve, which arouses an agent to action toward a goal, giving purpose and direction to behavior. Based on Maslow hierarchy of needs, an agent needs to satisfy a certain amount of needs at the current level as a condition to arise at the next stage -- upgrade and evolution. Especially, Deep Reinforcement Learning (DAL) can help AI agents (like robots) organize and optimize their behaviors and strategies to develop diverse Strategies based on their current state and needs (expected utilities or rewards). This paper introduces the new hierarchical needs-driven Learning systems based on DAL and investigates the implementation in the single-robot with a novel approach termed Bayesian Soft Actor-Critic (BSAC). Then, we extend this topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions.